Efficient Landmark-Based Candidate Generation for kNN Queries on Road Networks

  • Tenindra AbeywickramaEmail author
  • Muhammad Aamir Cheema
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10178)


The k nearest neighbor (kNN) query on road networks finds the k closest points of interest (POIs) by network distance from a query point. A past study showed that a kNN technique using a simple Euclidean distance heuristic to generate candidate POIs significantly outperforms more complex techniques. While Euclidean distance is an effective lower bound when network distances represent physical distance, its accuracy degrades greatly for metrics such as travel time. Landmarks have been used to compute tighter lower bounds in such cases, however past attempts to use them in kNN querying failed to retrieve candidates efficiently. We present two techniques to address this problem, one using ordered Object Lists for each landmark and another using a combination of landmarks and Network Voronoi Diagrams (NVDs) to only compute lower bounds to a small subset of objects that may be kNNs. Our extensive experimental study shows these techniques (particularly NVDs) significantly improve on the previous best techniques in terms of both heuristic and query time performance.


Road networks Nearest neighbor Landmark Lower Bounds 



We sincerely thank the anonymous reviewers for their feedback which helped improve our work. The research of Muhammad Aamir Cheema is supported by ARC DE130101002 and DP130103405. Tenindra Abeywickrama is supported by an Australian Government RTP Scholarship.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Monash UniversityMelbourneAustralia

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